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Dependency-Aware Task Offloading Based on Application Hit Ratio
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AI
DOI：10.1109/TSC.2024.3495510.png)
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Abstract
En 中文
Mobile devices commonly offload latency-sensitive applications to edge servers to meet low-latency requirements. However, existing studies overlook dependency and application hit ratio considerations, hindering effective offloading for multi-applications and multi-tasks. To this end, this article proposes a Dependent task offloading and Service placement Optimization (DSO) method to maximize the application hit ratio, thereby providing high-quality service. The proposed DSO includes Improved Multi-Agent Q-Learning (IMAQL) and greedy algorithms. IMAQL optimizes service placement via Q-learning, while the greedy algorithm schedules task offloading. Extensive experiments on public datasets demonstrate that the DSO method enhances the application hit ratio by 4.7% to 11.7% and reduces the completion time by about 3.4% to 4.9% compared to alternative approaches.
Keywords:
Servers
Mobile handsets
Navigation
Q-learning
Optimization
Base stations
Low latency communication
Indexes
Edge computing
Water resources
service placement
dependency
application hit ratio
improved multi-agent Q-learning algorithm
Journal
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